Integrated Deep Learning Engine (IDLE)

IDLE provides nice cross-compiler for * TensorFlow and * Caffe with simple format, called UDLI (Unified Deep Learning Interface).
Overall architecture of IDLE is introduced in * Taewoo Kim, Eunju Yang, Soyoon Bae and Chan-Hyun Youn, “IDLE: Integrated Deep Learning Engine with Adaptive Task Scheduling on Heterogeneous GPUs”, to appear in IEEE TENCON, 2018

Getting Started

Dependencies

• Ubuntu 16.04 (>=)
• python 2.7
• TensorFlow-gpu >= 1.90
• Caffe 1.0
• cuDNN-6.0
• CUDA 9.0

Install prerequisite : caffe, pyCaffe, TensorFlow

Caffe

Python 2.7

$ sudo apt-get install python-pip python-dev 

Liberaries.

$ sudo apt-get update
$ sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
$ sudo apt-get install --no-install-recommends libboost-all-dev

Install CUDA : * CUDA 9.0

BLAS.

$ sudo apt-get install libatlas-base-dev

Other dependencies

$ sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev

Install Caffe

// Download source code
$ cd {workspace}
$ git clone https://github.com/BVLC/caffe.git
$ cd caffe
$ cp Makefile.config.example Makefile.config

// Change Make configuration --> check python include, lib path
$ vi Makefile.config
# NOTE: this is required only if you will compile the python interface. # We need to be able to find Python.h and numpy/arrayobject.h. PYTHON_INCLUDE := /usr/include/python2.7 \
/usr/local/lib/python2.7/dist-packages/numpy/core/include/

# We need to be able to find libpythonX.X.so or .dylib. PYTHON_LIB := /usr/lib

// Compile
$ make clean
$ make all
$ make test
$ make runtest

Install * PyCaffe

• Dependencies

$ cd {workspace}/caffe/python
$ for req in $(cat requirements.txt); do sudo pip install $req; done

• Setting environment variables

$ echo export PYTHONPATH={caffe_path}/python:$PYTHONPATH >> ~/.bashrc
$ echo export PYTHONPATH={python_path}:$PYTHONPATH >> ~/.bashrc
$ echo export CAFFE_ROOT={caffe_path} >> ~/.bashrc
$ echo export LD_LIBRARY_PATH=/usr/local/cuda/lib64:$LD_LIBRARY_PATH >> ~/.bashrc $ source ~/.bashrc

• Recompiile

$ make clean
$ make pycaffe

• Test PyCaffe

$ python
>> import caffe

TensorFlow

$ sudo apt-get install python-pip python-dev
$ pip install tensorflow-gpu

Python dependency module

Parmiko

$ pip install parmiko

Pillow

$ pip install pillow

Setting SSH Keys (for distributed training)

To support distributed training, all workers should share ssh-key each other.

• SSH key generation (local machine)

$ ssh-keygen -t -rsa
$ ls -al ~/.ssh

• send each ssh-key to all workers

$ scp ~/.ssh/id_rsa.pub (remote_machine_id@ip_address):($destination)

• register each worker key to authorized_keys

$ cat $destination/id_rsa.pub >> ~/.ssh/authorized_keys

Install IDLE

Install IDLE on your environment.

$ cd {working directory}
$ git clone https://github.com/KAIST-NCL/IDLE.git
$ echo export PYTHONPATH={working directory}:$PYTHONPATH >> ~/.bashrc
$ source !$

How to Run

Sample DL-MDL files are wtored under /model folder. all files with dlmdl extension shows the examples.

Edit sample DLMDL file

Edit the sample files for your environment.

Running IDLE

$ cd {working directory}
$ python src/UDLI.py --framework=tf --input=model/VGG -r

arguments are described in below

Argument	Explanation	Example
--framework	Choose framework	--framework = tf , --framework=caffe
--input	Path of input file (.dlmdl file)	--input=model/VGG.dlmdl
-r	Execution or not (If -r is set, execution is conducted right after compilation	-r
--compile_out	compile output path	--compile_out=output/VGG
--log_out	path to save TensorFlow log	--log_out=/tmp/IDLE_LOG/TF

License

This project is released under the *BSD 2-Clause license, see LICENSE for more information.